CMRM adds a conformal quantile regularization on prediction margins to any loss, improving noisy-label classification accuracy up to 3.39% across methods and benchmarks while preserving performance at zero noise.
T., Doucet, A., et al
6 Pith papers cite this work. Polarity classification is still indexing.
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ST-BCP tightens the coverage bound in Backward Conformal Prediction by applying a computable data-dependent transformation to nonconformity scores, reducing the average gap from 4.20% to 1.12% on benchmarks while proving superiority over the identity baseline.
Human-to-robot transfer learning with conformal prediction improves robot assembly action segmentation Edit score from 70.50 to 80.70 using only 16 robot demonstrations.
RareCP improves interval efficiency for time series conformal prediction by retrieving and weighting regime-specific calibration examples while adapting to drift and maintaining coverage.
Branched Normalizing Flow improves conditional coverage robustness of conformal prediction under distribution shift by normalizing test inputs to the calibration distribution and mapping prediction sets back.
Introduces SCQ and P-TAMS for structure-adaptive conformal inference under pairwise exchangeability, claiming finite-sample FDR control for large-scale OOD testing.
citing papers explorer
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Conformal Margin Risk Minimization: An Envelope Framework for Robust Learning under Label Noise
CMRM adds a conformal quantile regularization on prediction margins to any loss, improving noisy-label classification accuracy up to 3.39% across methods and benchmarks while preserving performance at zero noise.
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ST-BCP: Tightening Coverage Bound for Backward Conformal Prediction via Non-Conformity Score Transformation
ST-BCP tightens the coverage bound in Backward Conformal Prediction by applying a computable data-dependent transformation to nonconformity scores, reducing the average gap from 4.20% to 1.12% on benchmarks while proving superiority over the identity baseline.
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Uncertainty-Aware Intention Prediction for Human-to-Robot Assembly Teleoperation
Human-to-robot transfer learning with conformal prediction improves robot assembly action segmentation Edit score from 70.50 to 80.70 using only 16 robot demonstrations.
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RareCP: Regime-Aware Retrieval for Efficient Conformal Prediction
RareCP improves interval efficiency for time series conformal prediction by retrieving and weighting regime-specific calibration examples while adapting to drift and maintaining coverage.
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Robust Conditional Conformal Prediction via Branched Normalizing Flow
Branched Normalizing Flow improves conditional coverage robustness of conformal prediction under distribution shift by normalizing test inputs to the calibration distribution and mapping prediction sets back.
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Structure-Adaptive Conformal Inference for Large-Scale Out-of-Distribution Testing
Introduces SCQ and P-TAMS for structure-adaptive conformal inference under pairwise exchangeability, claiming finite-sample FDR control for large-scale OOD testing.